Created filtered CSVs and static logo plotsĀ¶
InĀ [1]:
# Imports
import os
import warnings
import dmslogo
import pandas as pd
import seaborn as sns
import matplotlib.colors
import matplotlib.pyplot as plt
# Plotting colors
tol_muted_adjusted = [
"#000000",
"#CC6677",
"#1f78b4",
"#DDCC77",
"#117733",
"#882255",
"#88CCEE",
"#44AA99",
"#999933",
"#AA4499",
"#DDDDDD",
]
# Create color palette
def color_gradient_hex(start, end, n):
"""Color function from polyclonal"""
cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
name="_", colors=[start, end], N=n
)
return [matplotlib.colors.rgb2hex(tup) for tup in cmap(list(range(0, n)))]
# Seaborn style settings
sns.set(rc={
"figure.dpi":300,
"savefig.dpi":300,
"svg.fonttype":"none",
})
sns.set_style("ticks")
sns.set_palette(tol_muted_adjusted)
# Suppress warnings
warnings.simplefilter("ignore")
File paths for data:
InĀ [2]:
# this cell is tagged as `parameters` for papermill parameterization
func_scores = None
antibody_escape_dir = None
filtered_csv_dir = None
escape_plots_dir = None
times_seen = None
n_selections = None
frac_models = None
cell_entry_default = None
filtered_func_scores = None
InĀ [3]:
# Parameters
func_scores = "results/func_effects/averages/293T_entry_func_effects.csv"
filtered_func_scores = "results/filtered_CSVs/293T_entry_filtered_mut_effects.csv"
antibody_escape_dir = "results/antibody_escape/averages/"
filtered_csv_dir = "results/filtered_CSVs/"
escape_plots_dir = "results/escape_plots/"
times_seen = 2
n_selections = 8
frac_models = 1
cell_entry_default = -3
InĀ [4]:
# # Uncomment for running interactive
# func_scores = "../results/func_effects/averages/293T_entry_func_effects.csv"
# antibody_escape_dir = "../results/antibody_escape/averages/"
# filtered_csv_dir = "../results/filtered_CSVs/"
# escape_plots_dir = "../results/escape_plots/"
# times_seen = 2
# n_selections = 8
# frac_models = 1
# cell_entry_default = -3
# filtered_func_scores = "../results/filtered_CSVs/293T_entry_filtered_mut_effects.csv"
Process antibody escape data and create both a filtered CSV file and antibody escape plot
InĀ [5]:
# Initialize list of antibodies and sites for further analysis
GPC_A_akin_antibodies = [
"S43711",
"S44428",
"S44446",
"S44478",
]
GPC_A_akin_escape_sites = []
GPC_A_hybrid_antibodies = [
"S43720",
"S44433",
]
GPC_A_hybrid_escape_sites = []
GPC_C_hybrid_antibodies = [
"S4378",
"S43727",
]
GPC_C_hybrid_escape_sites = []
def get_filtered_csv_and_logo_plot(
antibody_name,
func_effects_file,
min_times_seen,
min_func_score,
min_n_selections,
min_frac_models,
filtered_file_dir = None,
floor = False,
sites = None,
num_sites = 20,
color_contact_sites = False,
contacts_file = None,
plots_dir = None,
ax = [],
):
"""
Function that filters and writes an antibody escape csv.
Also creates summed escape profiles and logoplots.
"""
# Load data
escape_df = pd.read_csv(antibody_escape_dir+antibody_name+"_mut_effect.csv")
func_effects = pd.read_csv(func_effects_file)
# Create mutation column to match antibody df
# **
# Pipeline does not filter functional scores
# prior to filtering antibody selections
# **
func_effects["site"] = func_effects["site"].astype(str)
func_effects["mutation"] = func_effects["wildtype"] + func_effects["site"] + func_effects["mutant"]
func_scores_filter = func_effects.loc[func_effects["effect"] >= min_func_score]["mutation"].tolist()
# Filter escape df for min times seen
# min fraction of models, and no stop codons
escape_df = (
escape_df.loc[
(escape_df["times_seen"] >= min_times_seen)
&
(escape_df["frac_models"] >= min_frac_models)
&
(escape_df["mutant"] != "*")
]
.rename(columns={"escape_median" : "escape"})
.reset_index(drop=True)
)
# Drop all but a few columns
escape_df = escape_df[[
"site",
"wildtype",
"mutant",
"mutation",
"escape",
]]
# Mark mutations that are below functional cutoff
# and replace measurements with NaN
escape_df["poor_cell_entry"] = (
escape_df["mutation"].apply(lambda x: False if x in func_scores_filter else True)
)
escape_df["escape"] = (
escape_df.apply(lambda x: float("NaN") if (x["poor_cell_entry"] == True) else x["escape"], axis=1)
)
# Create a floored escape column
escape_df["floored_escape"] = escape_df["escape"].clip(lower=0)
# Write filtered escape to csv
if filtered_file_dir != None:
escape_df.to_csv(filtered_file_dir+antibody_name+"_filtered_mut_effects.csv", index=False)
# Show only positive escape values
if floor == True:
escape_df["escape"] = escape_df["floored_escape"]
# Add site escape column
summed_df = (
escape_df
.groupby(["site", "wildtype"])
.aggregate({
"escape" : "sum",
})
.rename(columns={"escape" : "site_escape"})
.reset_index()
)
escape_df = (
escape_df.merge(
summed_df,
how="left",
on=["site", "wildtype"],
validate="many_to_one",
)
)
# If not sites are given, then show top summed escape sites
if sites == None:
sites = sorted(summed_df.nlargest(num_sites, "site_escape")["site"].tolist())
if antibody_name in GPC_A_akin_antibodies:
GPC_A_akin_escape_sites.append(sorted(summed_df.nlargest(10, "site_escape")["site"].tolist()))
if antibody_name in GPC_A_hybrid_antibodies:
GPC_A_hybrid_escape_sites.append(sorted(summed_df.nlargest(25, "site_escape")["site"].tolist()))
if antibody_name in GPC_C_hybrid_antibodies:
GPC_C_hybrid_escape_sites.append(sorted(summed_df.nlargest(25, "site_escape")["site"].tolist()))
# Mark escape sites for each antibody to show
escape_df["show_site"] = escape_df.apply(lambda x: True if x["site"] in sites else False, axis=1)
# Shade contact sites in logo plot
if color_contact_sites == True:
# **
# Currently there are no known structures for these antibodies
contacts_df = pd.read_csv(contacts_file)
shade_sites = list(contacts_df.loc[contacts_df["distance"] == 4]["position"].unique())
escape_df["shade_site"] = escape_df.apply(lambda x: "#DDCC77" if x["site"] in shade_sites else None, axis=1)
# **
else:
escape_df["shade_site"] = None
escape_df["shade_alpha"] = 0.35
# Merge functional and escape dfs
func_effects["site"] = func_effects["site"].astype("int")
func_effects = func_effects.loc[func_effects["mutant"] != "*"] # remove stop codons
func_effects["effect"] = func_effects["effect"].clip(upper=0, lower=(min_func_score + (min_func_score*0.05))) # clip scores
escape_df = (
escape_df.merge(
func_effects[[
"site",
"wildtype",
"mutant",
"mutation",
"effect",
]],
how="left",
on=["site", "wildtype", "mutant", "mutation"],
validate="one_to_one",
)
)
# Missing functional values are filled as min to make less visible
escape_df["effect"] = escape_df["effect"].fillna(min_func_score + (min_func_score*0.05))
# Add color column for logo plots
func_color_map = dmslogo.colorschemes.ValueToColorMap(
minvalue=func_effects["effect"].min(),
maxvalue=func_effects["effect"].max(),
cmap=matplotlib.colors.ListedColormap(color_gradient_hex("white", "#000000", n=20))
)
escape_df = (
escape_df.assign(
color=lambda x: x["effect"].map(func_color_map.val_to_color)
)
)
# Add wildtype to each site for logo plot
escape_df["wt_site"] = escape_df["wildtype"] + escape_df["site"].map(str)
# Fill in missing sites because logo plot requires
# a sequential list of sites
seen_list = [False]*491
for index in range(len(escape_df.index)):
site = escape_df.at[index, "site"] - 1
seen_list[site] = True
for index, seen in enumerate(seen_list):
if seen == False:
site = index + 1
# Add missing sites
escape_df.loc[len(escape_df.index)] = [
site,
"X",
"X",
"X"+str(site)+"X",
float("nan"),
False,
float("nan"),
float("nan"),
False,
None,
0.35,
min_func_score + (min_func_score*0.05),
"#ffffff",
"X"+str(site),
]
# Sort by site
escape_df = (
escape_df
.sort_values(by="site")
.astype({"mutant" : "str"})
.reset_index(drop=True)
)
# Create fig if no ax passed to function
if len(ax) == 0:
# Set figure size and subplots
fig, axes = plt.subplots(
1,
3,
gridspec_kw={"width_ratios":[1,3,0.1]},
figsize=(12, 2.5),
)
# Adjust spacing of subplots
fig.subplots_adjust(
wspace=0.3,
top=0.8,
bottom=0.3,
)
# Plot escape profiles and logo plots
# Lineplot
_, lineplot = dmslogo.draw_line(
escape_df,
x_col="site",
height_col="site_escape",
show_col="show_site",
ax=axes[0],
show_color="#CC6677",
linewidth=1,
)
# Logoplot
_, logoplot = dmslogo.draw_logo(
escape_df.query("show_site == True"),
x_col="site",
letter_col="mutant",
letter_height_col="escape",
ax=axes[1],
xtick_col="wt_site",
color_col="color",
shade_color_col="shade_site",
shade_alpha_col="shade_alpha",
draw_line_at_zero="if_negative",
)
else:
# Plot escape profiles and logo plots
# Lineplot
_, lineplot = dmslogo.draw_line(
escape_df,
x_col="site",
height_col="site_escape",
show_col="show_site",
ax=ax[0],
show_color="#CC6677",
linewidth=1,
)
# Logoplot
_, logoplot = dmslogo.draw_logo(
escape_df.query("show_site == True"),
x_col="site",
letter_col="mutant",
letter_height_col="escape",
ax=ax[1],
xtick_col="wt_site",
color_col="color",
shade_color_col="shade_site",
shade_alpha_col="shade_alpha",
draw_line_at_zero="if_negative",
)
# Format lineplot
lineplot.set(ylabel="escape", xlabel=None)
lineplot.set_xlim(1,491)
xticks = [100, 200, 300, 400]
lineplot.set_xticks(xticks)
x_labels = [
"100",
"200",
"300",
"400",
]
lineplot.set_xticklabels(labels=x_labels, rotation=90, horizontalalignment="center")
lineplot.set_title(
antibody_name[0:4] + "." + antibody_name[4:],
)
# Format logoplot
logoplot.set(ylabel=None, xlabel=None)
logoplot.set_title(
"antibody " + antibody_name[0:4] + "." + antibody_name[4:],
)
if len(ax) == 0:
# Create color bar for func effects
_, ax_scale = func_color_map.scale_bar(
orientation="vertical",
ax=axes[2],
)
else:
# Create color bar for func effects
_, ax_scale = func_color_map.scale_bar(
orientation="vertical",
ax=ax[2],
)
ax_scale.set_yticks([-3.0, -2.5, -2.0, -1.5, -1.0, -0.5, 0])
scale_labels = ["-3.0", "-2.5", "-2.0", "-1.5", "-1.0", "-0.5", "ā„0"]
ax_scale.set_yticklabels(labels=scale_labels)
ax_scale.set_title("effect on\ncell entry")
if plots_dir != None:
fig.savefig(plots_dir + antibody_name + "_escape_plot.svg")
InĀ [6]:
antibodies = [
"S4378",
"S43711",
"S43720",
"S43727",
"S43742",
"S44428",
"S44433",
"S44446",
"S44478",
]
for antibody in antibodies:
# Make output dir if doesn't exist
if not os.path.exists(filtered_csv_dir):
os.mkdir(filtered_csv_dir)
# Make output dir if doesn't exist
if not os.path.exists(escape_plots_dir):
os.mkdir(escape_plots_dir)
get_filtered_csv_and_logo_plot(
antibody,
func_scores,
times_seen,
cell_entry_default,
n_selections,
frac_models,
filtered_file_dir = filtered_csv_dir,
floor = True,
plots_dir = escape_plots_dir,
)
Escape summary plot for GPC-A akin antibodies
InĀ [7]:
# Flatten list of escape sites
GPC_A_escape_sites = [x for xs in GPC_A_akin_escape_sites for x in xs]
# Combined top sites from each antibody
sites_to_show = list(set(GPC_A_escape_sites))
print("Escape sites to show for GPC-A antibodies")
print(sites_to_show)
# Set figure size and subplots
fig, axes = plt.subplots(
4,
3,
gridspec_kw={"width_ratios":[1,3,0.1]},
figsize=(12, 12),
sharex="col",
)
# Adjust spacing of subplots
fig.subplots_adjust(
wspace=0.3,
hspace=0.35,
top=0.8,
bottom=0.3,
)
for index,antibody in enumerate(GPC_A_akin_antibodies):
get_filtered_csv_and_logo_plot(
antibody,
func_scores,
times_seen,
cell_entry_default,
n_selections,
frac_models,
floor = True,
sites = sites_to_show,
ax = axes[index]
)
# Make output dir if doesn't exist
if not os.path.exists(escape_plots_dir + "subset_analysis_plots/"):
os.mkdir(escape_plots_dir + "subset_analysis_plots/")
# Save figure
fig.savefig(escape_plots_dir + "subset_analysis_plots/" + "GPC-A_antibodies_escape_plot.svg")
Escape sites to show for GPC-A antibodies [134, 135, 143, 144, 160, 74, 76, 79, 88, 96, 225, 226, 227, 228, 229, 102, 230, 236, 367]
Escape summary plot for GPC-A hybrid antibodies
InĀ [8]:
# Flatten list of escape sites
escape_sites = [x for xs in GPC_A_hybrid_escape_sites for x in xs]
# Combined top sites from each antibody
sites_to_show = list(set(escape_sites))
print("Escape sites to show for GPC-A hybrid antibodies")
print(sites_to_show)
# Set figure size and subplots
fig, axes = plt.subplots(
2,
3,
gridspec_kw={"width_ratios":[1,4,0.1]},
figsize=(16, 6),
sharex="col",
)
# Adjust spacing of subplots
fig.subplots_adjust(
wspace=0.25,
hspace=0.35,
top=0.8,
bottom=0.3,
)
for index,antibody in enumerate(GPC_A_hybrid_antibodies):
get_filtered_csv_and_logo_plot(
antibody,
func_scores,
times_seen,
cell_entry_default,
n_selections,
frac_models,
floor = True,
sites = sites_to_show,
ax = axes[index]
)
# Make output dir if doesn't exist
if not os.path.exists(escape_plots_dir + "subset_analysis_plots/"):
os.mkdir(escape_plots_dir + "subset_analysis_plots/")
# Save figure
fig.savefig(escape_plots_dir + "subset_analysis_plots/" + "GPC-A_hybrid_antibodies_escape_plot.svg")
Escape sites to show for GPC-A hybrid antibodies [129, 133, 134, 135, 139, 140, 142, 143, 144, 147, 148, 149, 153, 283, 156, 157, 160, 74, 76, 77, 79, 338, 88, 120, 224, 98, 227, 228, 229, 236, 367, 240, 248, 250, 123, 253, 254, 127]
Escape summary plot for GPC-C hybrid antibodies
InĀ [9]:
# Flatten list of escape sites
escape_sites = [x for xs in GPC_C_hybrid_escape_sites for x in xs]
# Combined top sites from each antibody
sites_to_show = list(set(escape_sites))
print("Escape sites to show for GPC-C hybrid antibodies")
print(sites_to_show)
# Set figure size and subplots
fig, axes = plt.subplots(
2,
3,
gridspec_kw={"width_ratios":[1,4,0.1]},
figsize=(16, 6),
sharex="col",
)
# Adjust spacing of subplots
fig.subplots_adjust(
wspace=0.25,
hspace=0.35,
top=0.8,
bottom=0.3,
)
for index,antibody in enumerate(GPC_C_hybrid_antibodies):
get_filtered_csv_and_logo_plot(
antibody,
func_scores,
times_seen,
cell_entry_default,
n_selections,
frac_models,
floor = True,
sites = sites_to_show,
ax = axes[index]
)
# Make output dir if doesn't exist
if not os.path.exists(escape_plots_dir + "subset_analysis_plots/"):
os.mkdir(escape_plots_dir + "subset_analysis_plots/")
# Save figure
fig.savefig(escape_plots_dir + "subset_analysis_plots/" + "GPC-C_hybrid_antibodies_escape_plot.svg")
Escape sites to show for GPC-C hybrid antibodies [129, 257, 131, 134, 135, 139, 142, 143, 144, 273, 402, 147, 148, 149, 398, 152, 153, 160, 166, 325, 334, 337, 338, 343, 120, 119, 248, 121, 250, 123, 252, 253, 254, 127]
Get filtered CSV for functional effects
InĀ [10]:
# Read CSV file
func_scores = pd.read_csv(func_scores)
# Filter for minimum selections, times seen
func_scores = (
func_scores.loc[
(
(func_scores["n_selections"] >= n_selections)
&
(func_scores["times_seen"] >= times_seen)
)
|
(func_scores["wildtype"] == func_scores["mutant"])
]
.drop(columns=[
"effect_std",
"times_seen",
"n_selections",
])
.reset_index(drop=True)
)
# Save CSV file
func_scores.to_csv(filtered_func_scores, index=False)